Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore, Singapore.
JMIR Mhealth Uhealth. 2020 Jan 31;8(1):e16409. doi: 10.2196/16409.
Greater adoption of wearable devices with multiple sensors may enhance personalized health monitoring, facilitate early detection of some diseases, and further scale up population health screening. However, few studies have explored the utility of data from wearable fitness trackers in cardiovascular and metabolic disease risk prediction.
This study aimed to investigate the associations between a range of activity metrics derived from a wearable consumer-grade fitness tracker and major modifiable biomarkers of cardiometabolic disease in a working-age population.
This was a cross-sectional study of 83 working adults. Participants wore Fitbit Charge 2 for 21 consecutive days and went through a health assessment, including fasting blood tests. The following clinical biomarkers were collected: BMI, waist circumference, waist-to-hip ratio, blood pressure, triglycerides (TGs), high-density lipoprotein (HDL) and low-density lipoprotein cholesterol, and blood glucose. We used a range of wearable-derived metrics based on steps, heart rate (HR), and energy expenditure, including measures of stability of circadian activity rhythms, sedentary time, and time spent at various intensities of physical activity. Spearman rank correlation was used for preliminary analysis. Multiple linear regression adjusted for potential confounders was used to determine the extent to which each metric of activity was associated with continuous clinical biomarkers. In addition, pairwise multiple regression was used to investigate the significance and mutual dependence of activity metrics when two or more of them had significant association with the same outcome from the previous step of the analysis.
The participants were predominantly middle aged (mean age 44.3 years, SD 12), Chinese (62/83, 75%), and male (64/83, 77%). Blood biomarkers of cardiometabolic disease (HDL cholesterol and TGs) were significantly associated with steps-based activity metrics independent of age, gender, ethnicity, education, and shift work, whereas body composition biomarkers (BMI, waist circumference, and waist-to-hip ratio) were significantly associated with energy expenditure-based and HR-based metrics when adjusted for the same confounders. Steps-based interdaily stability of circadian activity rhythm was strongly associated with HDL (beta=5.4 per 10% change; 95% CI 1.8 to 9.0; P=.005) and TG (beta=-27.7 per 10% change; 95% CI -48.4 to -7.0; P=.01). Average daily steps were negatively associated with TG (beta=-6.8 per 1000 steps; 95% CI -13.0 to -0.6; P=.04). The difference between average HR and resting HR was significantly associated with BMI (beta=-.5; 95% CI -1.0 to -0.1; P=.01) and waist circumference (beta=-1.3; 95% CI -2.4 to -0.2; P=.03).
Wearable consumer-grade fitness trackers can provide acceptably accurate and meaningful information, which might be used in the risk prediction of cardiometabolic disease. Our results showed the beneficial effects of stable daily patterns of locomotor activity for cardiometabolic health. Study findings should be further replicated with larger population studies.
更多地采用具有多种传感器的可穿戴设备可以增强个性化健康监测，有助于早期发现某些疾病，并进一步扩大人口健康筛查范围。然而，很少有研究探讨可穿戴健身追踪器的数据在心血管和代谢疾病风险预测中的应用。
本研究旨在探讨一系列源自可穿戴消费级健身追踪器的活动指标与工作年龄段人群中心血管代谢疾病的主要可改变生物标志物之间的关系。
这是一项对 83 名在职成年人的横断面研究。参与者佩戴 Fitbit Charge 2 连续 21 天，并进行健康评估，包括空腹血检。收集以下临床生物标志物：体重指数（BMI）、腰围、腰臀比、血压、甘油三酯（TGs）、高密度脂蛋白（HDL）和低密度脂蛋白胆固醇以及血糖。我们使用了一系列基于步数、心率（HR）和能量消耗的可穿戴设备衍生指标，包括昼夜活动节律稳定性、久坐时间和不同强度体力活动时间的指标。使用 Spearman 秩相关进行初步分析。使用调整潜在混杂因素的多元线性回归来确定每个活动指标与连续临床生物标志物的关联程度。此外，当两个或更多指标与前一步分析中的相同结果具有显著关联时，使用成对多元回归来研究活动指标的意义和相互依赖性。
参与者主要为中年人（平均年龄 44.3 岁，标准差 12），中国人（62/83，75%），男性（64/83，77%）。心血管代谢疾病的血液生物标志物（HDL 胆固醇和 TGs）与基于步数的活动指标显著相关，独立于年龄、性别、种族、教育程度和轮班工作，而身体成分生物标志物（BMI、腰围和腰臀比）与基于能量消耗和 HR 的指标显著相关，当调整相同混杂因素时。基于步数的昼夜活动节律日内稳定性与 HDL（每 10%变化 5.4；95%CI 1.8 至 9.0；P=.005）和 TG（每 10%变化-27.7；95%CI-48.4 至-7.0；P=.01）显著相关。平均每日步数与 TG 呈负相关（每 1000 步减少 6.8；95%CI-13.0 至-0.6；P=.04）。平均 HR 与静息 HR 之间的差值与 BMI（beta=-.5；95%CI-1.0 至-0.1；P=.01）和腰围（beta=-1.3；95%CI-2.4 至-0.2；P=.03）显著相关。
可穿戴消费级健身追踪器可以提供可接受的准确和有意义的信息，可用于心血管代谢疾病的风险预测。我们的研究结果表明，稳定的日常活动模式对心血管代谢健康有益。应进一步通过更大规模的人群研究来复制研究结果。
